Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Abstract

A comparative study of model predictive control (MPC) schemes and robust $$H_{\infty }$$ H ∞ state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

Lateral control for autonomous wheeled vehicles: A technical review

“…The simulation results favored the self‐adaptive controller; however, the $$$ {H}_{\infty } $$$ along with fuzzy controller performed just as well while PID was found to be the worst. Yang et al [74] compared $$$ {H}_{\infty } $$$ with MPC for trajectory tracking control using a 3DoF vehicle dynamics model. The two controllers were tested for curves and double lane change maneuvers in MATLAB‐Carsim co‐simulations.…”

Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach

“…To address the problem of automatic control in autonomous driving systems, a variety of automatic control strategies have been developed and implemented, e.g., proportional–integral–derivative (PID) control [ 7 , 8 , 9 ], robust control ( ) [ 10 ], fuzzy logic control [ 7 , 11 , 12 ], sliding-mode control (SMC) [ 13 , 14 ], Lyapunov-based control [ 14 , 15 ], linear parameter-varying (LPV) control [ 16 ], Takagi–Sugeno control [ 17 ], and linear quadratic regulator (LQR) control [ 18 ]. Model predictive control (MPC) stands out as a highly effective control strategy, relying on the dynamic model of the system to anticipate future states.…”

Robust path following control for autonomous vehicle considering actuator fault

“…Model based controllers are widely adopted for vehicle path tracking control. Especially for the model predictive control (MPC), which leads to its popularity for tracking problems in recent years due its ability to handle a wide variety of process control constraints systematically [3][4][5]. Ji applied a model predictive control method to track the reference trajectory established by the artificial potential field with constraints of vehicle handling limitation [6].…”